Alpha系统在我国某海域的GDOP值分析

利用Matlab软件计算Alpha系统台站在我国某海域的GDOP值,与采用补充导航台方案时相比较,验证该方案能够改善该海域GDOP值的分布,从而在理论上可以提高Alpha系统在该海域的导航定位精度。     

三自由度平面并联机器人误差分析

为了研究各种误差对并联机器人输出产生的影响,以三自由度平面并联机器人为例,利用并联机器人的环路特性,采用机器人微分理论,建立了误差正向模型.通过M atlab编程和计算机仿真,得出输出误差与机器人结构尺寸变化、位置和姿态变化的关系图.为该类型机器人的设计、制造、装配提供了指导依据.     

城市群客流生成预测精度控制模型研究

城市群客流生成预测是城市群轨道交通规划中客流预测的主要组成部分,其预测精度将直接影响轨道交通客流预测精度,进而影响轨道交通规划的合理性.在分析城市群客流生成特点的基础上,提出客流生成阶段预测精度控制分为小区划分精度控制、预测方法选择、建模精度控制及模型计算结果调整4个阶段的思路;详细论述后2个阶段的精度控制内容,在模型计算结果调整中提出区域对外交通调整、空间调整、发生总量平衡调整及发生吸引平衡调整4个调整内容,并建立调整模型.     

Cost overruns and demand shortfalls – Deception or selection?

A number of highly cited papers by Flyvbjerg and associates have shown that ex ante infrastructure appraisals tend to be overly optimistic. Ex post evaluations indicate a bias where investment costs are higher and benefits lower on average than predicted ex ante. These authors argue that the bias must be attributed to intentional misrepresentation by project developers. This paper shows that the bias may arise simply as a selection bias, without there being any bias at all in predictions ex ante, and that such a bias is bound to arise whenever ex ante predictions are related to the decisions whether to implement projects. Using a database of projects we present examples indicating that the selection bias may be substantial. The examples also indicate that benefit–cost ratios remain a useful selection criterion even when cost and benefits are highly uncertain, gainsaying the argument that such uncertainties render cost-benefit analyses useless.     

提高车辆动态称重仪精度的研究与试验

以提高车辆动态称重精度为目的，从两方面提出了改善措施：在分析车辆通过秤台时受力情况的基础上，设计了一种高精度的称重传感器；针对动态称重实际波形，设计了一种新的数据处理方法。通过对硬件和软件的改善，提高了动态称重精度。     

8XC196KC用于ABS轮速测量方法的改进研究

对ABS中速度测量的过程进行仔细的分析,提出了一种能够将精度和实时性相结合的速度测量方案,该方案能够较为有效地平衡精度和实时性之间的矛盾,并通过实际程序的执行验证了这一结论。这一测量方法不仅可用于ABS的速度测量,也可推广用于其他的速度或周期测量方案中。     

Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.     

船舶与海洋工程中的焊接变形预测方法研究概述

文章介绍了焊接变形预测理论的发展及现状,并将其归纳为基于实验和理论相结合的经验计算阶段、基于计算机数值模拟的模型分析阶段、基于新兴数据挖掘方法的研究应用阶段三个标志性阶段,详细论述了每个阶段预测技术在实际中应用的优劣.着重以BP、RBF人工神经网络为例阐述了神经网络等数据挖掘方法在焊接变形预测中的应用前景,并指出了目前存在的不足及未来亟待研究的方向.     

基于尺寸精度的铝合金减震器塔结构设计及优化

铝合金作为一种优质轻量化材料在轻量化车身上的应用越来越广泛,为了确保铝合金零部件的使用功能和性能,设计人员在设计前期就需要对零部件的精度进行设计分析和优化。文章基于一种压铸铝合金减震器塔的零部件,首先对该铝合金零部件精度进行分析摸底并识别出影响该铝合金零部件精度的两个关键要素,再分别针对两个关键要素进行优化设计,重点一是优化加强筋结构,以增加减震器塔本体的刚度和抗变形能力,另外是增加热处理工艺中夹具支撑块结构,通过外力支撑来增加抗变形能力。结果表明:合理的加强筋布置能有效提升减震器塔本体的刚度、强度和尺寸精度;热处理时需设计专用支撑夹具,且支撑夹具要保证所有重要安装面的支撑,避免铝合金减震器塔局部出现悬臂,通过外力支撑来防止变形。     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.